{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import required libraries NumPy, polynomial and matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate two random vectors\n",
    "v1=np.random.rand(10)\n",
    "v2=np.random.rand(10)\n",
    "\n",
    "# Creates a sequence of equally separated values\n",
    "sequence = np.linspace(v1.min(),v1.max(), num=len(v1)*10)\n",
    "\n",
    "# Fit the data to polynomial fit data with 4 degree of polynomial\n",
    "coefs = np.polyfit(v1, v2, 3)\n",
    "\n",
    "# Evaluate polynomial on given sequence\n",
    "polynomial_sequence = np.polyval(coefs,sequence)\n",
    "\n",
    "# plot the  polynomial curve \n",
    "plt.plot(sequence, polynomial_sequence)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix:\n",
      " [[2 4]\n",
      " [5 7]]\n",
      "Determinant: -5.999999999999998\n"
     ]
    }
   ],
   "source": [
    "# Import numpy \n",
    "import numpy as np\n",
    "\n",
    "# Create matrix using NumPy\n",
    "mat=np.mat([[2,4],[5,7]])\n",
    "print(\"Matrix:\\n\",mat)\n",
    "\n",
    "# Calculate determinant\n",
    "print(\"Determinant:\",np.linalg.det(mat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix:\n",
      " [[2 4]\n",
      " [5 7]]\n",
      "Inverse:\n",
      " [[-1.16666667  0.66666667]\n",
      " [ 0.83333333 -0.33333333]]\n"
     ]
    }
   ],
   "source": [
    "# Import numpy \n",
    "import numpy as np\n",
    "\n",
    "# Create matrix using NumPy\n",
    "mat=np.mat([[2,4],[5,7]])\n",
    "print(\"Matrix:\\n\",mat)\n",
    "\n",
    "# Find matrix inverse\n",
    "inverse = np.linalg.inv(mat)\n",
    "print(\"Inverse:\\n\",inverse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix A:\n",
      " [[1 1]\n",
      " [3 2]]\n",
      "Vector B: [200 450]\n"
     ]
    }
   ],
   "source": [
    "# Create matrix A and Vector B using NumPy\n",
    "A=np.mat([[1,1],[3,2]])\n",
    "print(\"Matrix A:\\n\",A)\n",
    "\n",
    "B = np.array([200,450]) \n",
    "print(\"Vector B:\", B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solution vector x: [ 50. 150.]\n"
     ]
    }
   ],
   "source": [
    "# Solve linear equations\n",
    "solution = np.linalg.solve(A, B) \n",
    "print(\"Solution vector x:\", solution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result: [[200. 450.]]\n"
     ]
    }
   ],
   "source": [
    "# Check the solution\n",
    "print(\"Result:\",np.dot(A,solution))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix:\n",
      " [[2 4]\n",
      " [5 7]]\n"
     ]
    }
   ],
   "source": [
    "# Import numpy \n",
    "import numpy as np\n",
    "\n",
    "# Create matrix using NumPy\n",
    "mat=np.mat([[2,4],[5,7]])\n",
    "print(\"Matrix:\\n\",mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvalues: [-0.62347538  9.62347538]\n",
      "Eigenvectors: [[-0.83619408 -0.46462222]\n",
      " [ 0.54843365 -0.885509  ]]\n"
     ]
    }
   ],
   "source": [
    "# Calculate the eigenvalues and eigenvectors\n",
    "eigenvalues, eigenvectors = np.linalg.eig(mat)\n",
    "print(\"Eigenvalues:\", eigenvalues) \n",
    "print(\"Eigenvectors:\", eigenvectors) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvalues: [-0.62347538  9.62347538]\n"
     ]
    }
   ],
   "source": [
    "# Compute eigenvalues \n",
    "eigenvalues= np.linalg.eigvals(mat)\n",
    "print(\"Eigenvalues:\", eigenvalues) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Left Singular Matrix: [[-0.70097269 -0.06420281 -0.7102924 ]\n",
      " [-0.6748668  -0.26235919  0.68972636]\n",
      " [-0.23063411  0.9628321   0.14057828]]\n",
      "Diagonal Matrix:  [8.42757145 4.89599358 0.07270729]\n",
      "Right Singular Matrix: [[-0.84363943 -0.48976369 -0.2200092 ]\n",
      " [-0.13684207 -0.20009952  0.97017237]\n",
      " [ 0.51917893 -0.84858218 -0.10179157]]\n"
     ]
    }
   ],
   "source": [
    "# import required libraries\n",
    "import numpy as np\n",
    "from scipy.linalg import svd\n",
    "\n",
    "# Create a matrix\n",
    "mat=np.array([[5, 3, 1],[5, 3, 0],[1, 0, 5]])\n",
    "\n",
    "# Perform matrix decomposition using SVD \n",
    "U, Sigma, V_transpose = svd(mat)\n",
    "\n",
    "print(\"Left Singular Matrix:\",U)\n",
    "print(\"Diagonal Matrix: \", Sigma)\n",
    "print(\"Right Singular Matrix:\", V_transpose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix: \n",
      " [[5 3 1]\n",
      " [5 3 1]\n",
      " [1 0 5]]\n",
      "Rank: 2\n"
     ]
    }
   ],
   "source": [
    "# import required libraries\n",
    "import numpy as np\n",
    "from numpy.linalg import matrix_rank\n",
    "\n",
    "# Create a matrix\n",
    "mat=np.array([[5, 3, 1],[5, 3, 1],[1, 0, 5]])\n",
    "\n",
    "# Compute rank of matrix\n",
    "print(\"Matrix: \\n\", mat)\n",
    "print(\"Rank:\",matrix_rank(mat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Matrix: \n",
      " [[0.87986252 0.48490635 0.43941716]\n",
      " [0.28841211 0.94874275 0.21350708]\n",
      " [0.4637613  0.08938804 0.85683912]]\n"
     ]
    }
   ],
   "source": [
    "# Import numpy \n",
    "import numpy as np\n",
    "\n",
    "# Create an array with random values\n",
    "random_mat=np.random.random((3,3))\n",
    "print(\"Random Matrix: \\n\",random_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import required libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create an numpy vector of size 5000 with value 0\n",
    "cash_balance = np.zeros(5000) \n",
    "\n",
    "cash_balance[0] = 500\n",
    "\n",
    "# Generate random numbers using Binomial\n",
    "samples = np.random.binomial(9, 0.5, size=len(cash_balance))\n",
    "\n",
    "# Update the cash balance\n",
    "for i in range(1, len(cash_balance)):\n",
    "    if samples[i] < 5:\n",
    "        cash_balance[i] = cash_balance[i - 1] - 1 \n",
    "    else:\n",
    "        cash_balance[i] = cash_balance[i - 1] + 1 \n",
    "\n",
    "# Plot the updated cash balance\n",
    "plt.plot(np.arange(len(cash_balance)), cash_balance)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import required library\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "sample_size=225000\n",
    "\n",
    "# Generate random values sample using normal distriubtion\n",
    "sample = np.random.normal(size=sample_size)\n",
    "\n",
    "# Create Histogram \n",
    "n, bins, patch_list = plt.hist(sample, int(np.sqrt(sample_size)), density=True) \n",
    "\n",
    "# Set parameters\n",
    "mu, sigma=0,1\n",
    "\n",
    "x= bins\n",
    "y= 1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (bins - mu)**2 / (2 * sigma**2) )\n",
    "\n",
    "# Plot line plot(or bell curve)\n",
    "plt.plot(x,y,color='red',lw=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create small, medium, and large samples for noymality test\n",
    "small_sample = np.random.normal(loc=10, scale=6, size=10)\n",
    "medium_sample = np.random.normal(loc=10, scale=6, size=100)\n",
    "large_sample = np.random.normal(loc=10, scale=6, size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for small\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create distribution plot\n",
    "sns.distplot(small_sample)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6FGNCZkFvLktdWx9lealRf9vAUM3PT0OAmpZep0sxJmQW9OaSnekYoHNgJC7m589JSnBTnJ1sH8iaqGJBby7Ze8HdHONlfv6c8oJ06jsHGfSNOl2KMSGxoDeX7L26dlITPRSkJzpdyowqL0hDCWzkZkw0sKA3l0RVebe2jfl5qXF3lWhJTjJet4vjNn1jooQFvbkkJ9r6ae4ZjrtpGwCPy0VZXiq1rRb0JjpY0JtL8m5wfj6ePogdr7wgjbY+H10DPqdLMWZKFvTmkrxX105hZhK5qV6nS3HEue0QbPrGRAMLenPRVAP3T/3Y/Ny4m58/pyA9kYwkD8ebbT29iXwW9OaiHWvuo73fx8cW5DpdimNEhCtmpVPT2sfomN350kQ2C3pz0d6tDdwkO56DHqBiVjpDI2PUdw44XYoxF2RBby7ae7XtzM1JoTg7xelSHFWen4ZL4JhN35gIZ0FvLsromLKjLjA/H++SvW5KslM41mwfyJrIZkFvLsqRph56hvysKbegh8D0TUPXIH3DfqdLMea8Qgp6EVkvItUiUiMiD09yXkTkkeD5gyKyfMJ5t4jsE5Ffhatw44wP5+dtRA/AFbPO3XXKpm9M5Joy6EXEDTwKbAAWAfeKyKIJzTYAFcHHJuCxCee/Bhy57GqN496rbWdBfioFGfGx//xU5mQlk+J12/SNiWihjOhXAjWqWqeqPuA5YOOENhuBpzVgB5AlIoUAIlIM3Al8P4x1GweMjI6x60RH3K+2Gc8lQuWsdI419zKmtszSRKZQgr4IODPueX3wWKht/hn4U+CC914TkU0isltEdre2toZQlplphxq66feNsmZBntOlRJTK2ekM+EY502HLLE1kCiXoJ7v0ceLQZdI2IvJJoEVV90z1Jqr6hKquUNUV+fn5IZRlZtr2422IwGqbn/8tFQXpuASOnrV5ehOZQgn6eqBk3PNioDHENmuBT4nISQJTPjeLyI8vuVrjqG01bSyek0FOnO5vcz7JXjeluakcPdvjdCnGTCqUoH8fqBCRMhHxAvcAmye02QzcH1x9sxroVtUmVf1zVS1W1dLg615X1d8PZwfMzBjw+dl7upO15TZtM5mFs9Np7hmms992szSRZ8qgV1U/8BCwlcDKmZ+qapWIPCAiDwSbbQHqgBrgSeAr01SvcciuEx2MjCrXW9BPauHsDACO2lWyJgJ5QmmkqlsIhPn4Y4+P+1qBB6f4Hm8Cb150hSYibK9pw+txcV1pjtOlRKS89ERyU71U2/SNiUB2ZawJyTvH21gxL5ukBLfTpUSshbPTqW3tp9+ukjURxoLeTKm1d5ijZ3u5vsKmbS7kysIMRseUt4/Z8mATWSzozZTObXtg8/MXNi83lRSvm5erzjpdijG/xYLeTGl7TRuZyQksnpPpdCkRze0SrizM4PUjLfj8F7w+0JgZZUFvLkhV2Xa8jTULcnG74vO2gRdj8ZwMeof9H/4WZEwksKA3F3SyfYDG7iFbPx+iBflppCV62GrTNyaCWNCbC9pWY/PzFyPB7eLGynxeqWq2e8maiGFBby5o2/FWirKSmZcb37cNvBjrl8ymvd/HnlOdTpdiDGBBby5gdEx5t7ad68vzELH5+VDdWFmA1+Niy6Emp0sxBrCgNxdwqKGb3iG/rZ+/SGmJHm6qzOfFQ002fWMiggW9Oa/twfn5NXajkYv2O8vm0No7zM4T7U6XYowFvTm/bcfbWFSYQW5aotOlRJ1bFs4ixevmPw7Y9I1xngW9mdSgb5Q9pzpt2uYSJXvd3LZoFi8dbmJk1C6eMs6yoDeTeq+uDd/oGOss6C/Z7yydQ9fAyIdLVI1xigW9mdQbR1tJTnCzssy2Jb5U667IIyPJw38cmHhDNmNmlgW9+QhV5Y3qFtaW55LosW2JL1Wix836JbPZevgsg75Rp8sxccyC3nxEbWs/9Z2D3FhZ4HQpUe8zy4vp943algjGURb05iPerG4B4MbKfIcriX4rS3Mozk7mZ3vqnS7FxLGQgl5E1otItYjUiMjDk5wXEXkkeP6giCwPHk8SkV0ickBEqkTkr8PdARN+b1a3UlGQRnG2bXtwuVwu4e7lxWyvbaOxa9DpckycmjLoRcQNPApsABYB94rIognNNgAVwccm4LHg8WHgZlVdBlwNrBeR1WGq3UyD/mE/O0+0c9NCm7YJl7uXF6MKL+xrcLoUE6dCGdGvBGpUtU5VfcBzwMYJbTYCT2vADiBLRAqDz/uCbRKCD7smPIJtr2ljZFRt2iaM5uamsLIsh5/vqUfV/vqbmRdK0BcBZ8Y9rw8eC6mNiLhFZD/QAryqqjsvvVwz3V4/2kJaoocV82xZZTj9p+XF1LX1s/d0l9OlmDgUStBPtm3hxGHJeduo6qiqXg0UAytFZMmkbyKySUR2i8ju1la7ubITRseUXx9p5sbKfLwe+5w+nO5YWkiq182zu047XYqJQ6H831wPlIx7XgxMvAJkyjaq2gW8Cayf7E1U9QlVXaGqK/LzbdrACfvPdNLW5+O2RbOcLiXmpCV62HhNEb862Ej3wIjT5Zg4E0rQvw9UiEiZiHiBe4DNE9psBu4Prr5ZDXSrapOI5ItIFoCIJAO3AkfDWL8Jo1c+aMbjEls/P03uWzmXoZExnt9nSy3NzJoy6FXVDzwEbAWOAD9V1SoReUBEHgg22wLUATXAk8BXgscLgTdE5CCBfzBeVdVfhbkPJkxe/aCZ1fNzyUxOcLqUmLSkKJNlxZk8s/O0fShrZpQnlEaquoVAmI8/9vi4rxV4cJLXHQSuucwazQyobe2jrrWfz3+s1OlSot4zO88/D78gP43n9zXwN1uOUpqXet52962aOx2lmThln7gZIDCaB7jV5uen1dLiLBI9LrshiZlRFvQGgFeqzrKkKIOirGSnS4lpXo+L5fOyOdzQQ8+QfShrZoYFvaGpe5C9p7v4xKLZTpcSF9bMz2VMlV0nOpwuxcQJC3rDS4cCOyvesbTQ4UriQ25aIpWz09l5ogO/3X3KzAALesOLh5pYODudBflpTpcSN9YsyKN/2M/B+m6nSzFxwII+zjV2DbLnVCeftNH8jFqQn0pBeiLv1rbZUksz7Szo49xLh4PTNldZ0M8kEWHNgjwau4eoa+t3uhwT4yzo49yLBxu5sjCD+TZtM+OumZtFWqKHt47Z3k5melnQx7HGrsBqG5u2cUaC28Xa8jxqWvpo6LSbkpjpE9KVsSY2/c//+ACAsTG94NWcZvqsKsvhrWMtvHWshftWzXO6HBOjbEQfp1SVvac7mZuTQm5aotPlxK2kBDery3KpauyhtXfY6XJMjLKgj1NVjT209A5zdUmW06XEvTXlebhdwjvHba7eTA8L+jj1wr4G3CIsLcp0upS4l5boYUVpNvtOd9E9aNsimPCzoI9D/tExfrm/kcrZ6aQk2sc0kWBdeT6Ksr2mzelSTAyyoI9D79S00dY3zDVzbdomUmSnellanMWuEx0MDPudLsfEGAv6OPT83gYykxOonJXudClmnBuuyMc3OsZ7toWxCTML+jjT0e9j6+GzfPqaIjxu+/FHktkZSSycnc67Ne302hbGJozs//Q48/M99fhGx7h3pd3BKBLdvLCAwZFR/nX7SadLMTHEgj6OqCrP7jrNtfOyqZxt0zaRqDg7hStnp/PkO3W2AseETUhBLyLrRaRaRGpE5OFJzouIPBI8f1BElgePl4jIGyJyRESqRORr4e6ACd3OEx3UtfXbaD7C3XLlLHqG/Dy17YTTpZgYMWXQi4gbeBTYACwC7hWRRROabQAqgo9NwGPB437gG6p6JbAaeHCS15oZ8uyu06QnebjTdqqMaHOyktmwZDZPbTtB14DP6XJMDAhlRL8SqFHVOlX1Ac8BGye02Qg8rQE7gCwRKVTVJlXdC6CqvcARoCiM9ZsQtfcN89Khs3zmmiKSvW6nyzFT+PqtV9Dn8/PkO3VOl2JiQChBXwScGfe8no+G9ZRtRKQUuAbYOdmbiMgmEdktIrtbW+1S8HB7ZudpfKNj/MHHbOOsaFA5O507ryrkh9tP0tFvo3pzeUIJepnk2MRb4lywjYikAT8Hvq6qPZO9iao+oaorVHVFfn5+CGWZUPn8Yzy94xQfvyKf8gL7EDZafP3WCgZHRvne27VOl2KiXChBXw+UjHteDDSG2kZEEgiE/E9U9flLL9VcqhcPNdLaO8wX15Y6XYq5COUF6WxcNoen3z1lO1uayxJK0L8PVIhImYh4gXuAzRPabAbuD66+WQ10q2qTiAjwA+CIqv5jWCs3IVFVfrDtBAvyU7mhwn5TijZfvaWCYf8oj71po3pz6aYMelX1Aw8BWwl8mPpTVa0SkQdE5IFgsy1AHVADPAl8JXh8LfAHwM0isj/4uCPcnTDnt/tUJ4cbevji2jJcrslm2Ewkm5+fxt3Li/nxjlM0dNldqMylCWnrQlXdQiDMxx97fNzXCjw4yeu2Mfn8vZkhj79ZS1ZKAp9ZboudotXXb7uCXx5o5J9ePcY/fHaZ0+WYKGRXxsawqsZuXjvawpfXlpHite2Io1VRVjL3r57H83vrOdbc63Q5JgpZ0Mew775RS3qih/vXlDpdirlMD95UTqrXw9+/XO10KSYKWdDHqJqWXrYcbuL+NfPITE5wuhxzmbJTvfyXj8/n10ea2X2yw+lyTJSxoI9R332jliSPmy+tLXO6FBMmX7q+jPz0RP7u5aMEPhYzJjQW9DHoeHMvv9jfwO+tmktuWqLT5ZgwSfF6+OotFbx/spPXj7Y4XY6JIhb0Mej/bK0mxevhKzeVO12KCbN7riuhNDeFv3+5mtExG9Wb0FjQx5g9pzp55YNmNt0wn5xUr9PlmDBLcLv4xu2VVDf38ot9DU6XY6KEBX0MUVX+7uWj5KUl8uXrbW4+Vt15VSFLijL4x1ePMewfdbocEwUs6GPIa0da2HWig6/eUk5qoq2bj1Uul/Bn6xfS0DXIj3ecdrocEwUs6GPE0Mgof/2rKioK0uwOUnFgXUU+a8tzefSNGruRuJmSDfui0DM7PzqKe+1IM2c6Bvny9WX8++56B6oy4TTZz3iiZcVZbK9p56vP7ue2RbM+cv6+VfYPvgmwEX0M6Oj38daxVq4qymRBfprT5ZgZUpydwlVFmWyraaXHbiRuLsCCPsqpKpsPNOAS4Q67F2zcuX3RLMYUXv2g2elSTASzoI9yu091cqy5j9sXz7KtDuJQbloia+bnsvd0J422jbE5Dwv6KNbZ7+PFQ03Mz0tl9fxcp8sxDrmxsoBkr5sth5psawQzKQv6KDWmys/31iPA3cuLcYlt+x+vkr1ubllYQF1bP0fP2jbG5qMs6KPU60dbqGvr586rCsm2K2Dj3sqyXPLSEnnpcJNtjWA+woI+ClWf7eH1oy0sn5vFtfOynS7HRAC3S7hjyWza+nzsPNHudDkmwljQR5kzHQP8dHc9hZlJfGpZEWJTNiaocnY6C/JTee1IC4M+2xrB/EZIQS8i60WkWkRqROThSc6LiDwSPH9QRJaPO/eUiLSIyOFwFh6POvt9fP6Hu1CU+1bOxeuxf6fNb0hwie3QyCivH7XlluY3prwyVkTcwKPAbUA98L6IbFbVD8Y12wBUBB+rgMeCfwL8K/D/gKfDV3b0CuWKx8n4/GP8YFsdTd1DfGltme0zbyZVmJnMitJs3qtrp/psL5Wz050uyUSAUIaEK4EaVa1TVR/wHLBxQpuNwNMasAPIEpFCAFV9G7B7n12GkdExnt11mvrOQT53XQmlealOl2Qi2O2LZpPocfPNXx625ZYGCC3oi4Az457XB49dbJsLEpFNIrJbRHa3trZezEtjms8/xo93nKK6uZe7ri5i8ZxMp0syES410cP6xbPZdaKDX+y3PetNaEE/2ad9E4cJobS5IFV9QlVXqOqK/Pz8i3lpzBoaGeVH752kpqWPu5cXcV1ZjtMlmShxbWk2y0qy+PaLR+m2fXDiXihBXw+UjHteDDReQhtzETr7fTz+Vi2n2vv53RUlXDvPQt6EziXCtzYuob1/mH969ZjT5RiHhRL07wMVIlImIl7gHmDzhDabgfuDq29WA92q2hTmWuPG6fZ+vvtWLT1DI3xhTRnLSn1sh6sAAAxPSURBVLKcLslEoauKM/m9VXN5+r2TVDV2O12OcdCUQa+qfuAhYCtwBPipqlaJyAMi8kCw2RagDqgBngS+cu71IvIs8B5QKSL1IvLlMPchZqgq79a28eQ7J0j0uHjg4wsoL7Bth82l+5PbF5Kd4uWbvzjMmF0xG7dCuvGIqm4hEObjjz0+7msFHjzPa++9nALjxdDIKC/sa+BQQzcLZ6fz2WtLSPa6nS7LRLnMlAQe3rCQP/nZQX66+wz32N3H4pJdcRMBTrX388jrx6lq7OYTi2bx+6vnWcibsLl7eTGrynL49otHaOq2rYzjkQW9g0bHlF8faeaJt+sQYNMNC/h4ZYHtRGnCyuUS/u7upYyMjfEXzx+ytfVxyILeIZ39Pp58p47Xj7ZwdUkWf3RzBXNzUpwuy8So0rxU/vQTC3mjupXn99ra+nhjNwd3wKGGbl7YV48qfG5Fia2qMTPiC2tK2XKoif/xH1Wsmp9DcbYNLOKFjehn0NDIKL/Y38Czu06Tn5bIH91cYSFvZozLJfzj716NKnz9uf34R8ecLsnMEAv6GVLT0sddj25n14kObqjIY9MNC8ixG4aYGTY3N4Vv3bWE3ac6+X9v1DhdjpkhNnUzA362p55v/uIwyV43n/9Yqe0oaBx11zVFvHWslUdeO86qslw+tsDuNxzrbEQ/jfqG/fy3f9vPH//7AZaVZPLS19ZZyJuI8D83LqYsL5UHn9lLQ5ctuYx1FvTTpKqxm9/5l238Yn8D//XWK/jJH65mVkaS02UZA0B6UgJP3L+CEf8Ym57ebXekinEW9GGmqjz93kk+/ei7DPj8PPOfV/O1Wytwu2xtvIksC/LT+M69V/NBUw9//O8H7KbiMczm6MOoo9/Hn/38IK9+0MxNlfn8w2eX2Z2gTES7eeEs/mLDlXx7yxGyUhL41l1L7D7EMciCPky217TxX/9tP10DI/zVnVfypbVluGwUb6LAf75hPm39w3zvrTpyUr184/ZKp0syYWZBf5l8/jH+7yvVPPFOHfPzUvnhF6+zu0CZqPPw+oV09Y/wL6/X4Bsd4+H1CyNuZH+p91se775V8bmpmwX9Zag+28sf//sBDjV0c9+quXzzzkW2GZmJSiLC//7MVXjcwvfeqqN7YIRvf/oq+2wpRljQX4Jh/yiPvl7Dd9+sJSM5ge/9wbV8YvFsp8sy5rK4XcK37lpCTqqXf3m9hvrOQb5zz9X2OVMMsKC/CKrK1qqz/M1LRznVPsBnrinirz65yK5wNTFDRPjG7ZUUZyfzzV9Wcecj2/iX+67hulK7lWU0s6APQeDOT+1859fH2XWyg8pZ6fz4y6u4viLP6dKMmRafu24ui+dk8uAze/nd773HPdfN5c/WV5KVErmDmr5hP01dg7T2DdPWN0z3wAg9Q34GfH58/jH8Y8rfvXyUBLeLjCQPOaleZmUksSA/lfJZ6ZTnpzE/P5WkhNibfrWgvwCff4xXPjjLU9tOsPd0FwXpiXzrriXcc10JHrddgmBi25KiTF786jr++dVj/PDdk2ytOssfrivj91fPIyMpwdHafP4xGrsGOdM5QH3nIPWdA3QOjHx4PinBRVayl4xkD/npiXg9LhJcQnlBGr5RpWdohPa+YQ43dvPS4SbOXUIgAmV5qSwrzmJZcSZLS7JYVJgR9eFvQT/B6Jiy93QnLx06yy/3N9De76M4O5n/ddcSPnttcdT/wI25GGmJHv7qk4u4+9pi/veWI/z9y9U89mYt/+naYu66uoilxZnTvjpnZHSM2tY+dp/s4Eww1Jt7hj4M56yUBIqzU1g9P5k5WckUpCeSluiZtK7JVt0MjYxyoq2fmpY+alr6qGrsYVtNGy/sC+zb73EJCwvTWVqcxdXFWSwtyaSiID2qPqiWUO42IyLrge8AbuD7qvq3E85L8PwdwADwBVXdG8prJ7NixQrdvXv3RXbl0gyNjHKsuZcDZ7rYcaKDHbXttPf78Lpd3LQwn3tXzmVdRX7YfqjhWCJmTCimYynhofpuHn+7llermvGNjjE3J4W15Xl8bEEuVxVlMjcn5ZL/XxkaGaWpe4j6zgGONfdxpKmHI009HG/uwxfcUjkpwUVJdgrF2ckUB/9Mv4jfLkL9b6KqnO0Z4sCZbg7Wd3GgvouD9d30DvkBSPG6WTInk6XFmVxZmEFpXiqluSnkpHodW5YqIntUdcVk56Yc0YuIG3gUuA2oB94Xkc2q+sG4ZhuAiuBjFfAYsCrE14ZN9+AIPv8YvtExhkdGg3+O0T04QueAj66BETr6fbT3D3OmY5AzHQOcbO//cGRQmJnEuoo8brlyFjdW5l/UXyBj4sFVxZk8et9yugdGeLmqiVeqmvnVgUae3RUYwCR6XJTmpjIrM4mC9ESSE9x4PS4SPS4SPW5cAn0+P31DfvqH/fQNj9LWN0xD1yCtvcO/9V55aYksmpPB9RV5LCrM4FTbADlp3hm51aaIUJiZTGFmMuuXBFbUjY0pJ9v7OVDf9eE/AE/vOIXP/5t9/dMTPczLS2F2RjJ5aV5y07zkpiaSm+YlLdFDstdNitdDitdNcoIbt0vwuAR38OFxu0hLDP9ESyjfcSVQo6p1wf8AzwEbgfFhvRF4WgO/HuwQkSwRKQRKQ3ht2Kz89q8Z9k99M4XAr3rJVM5O55PL5nDl7HQWz8mkJCc54i4SMSYSZaYk8Lnr5vK56+biHx3jSFMvR872UH22l9MdgamV4829DI2M4vOPMRz8MBQC/xikJXpIDT5yUhO4ubKAouxkirKSKcpOZkF+Gvnpv72s0+nfhl0uYX5+GvPz0/j0NcVAYFrp3IDxZNsAp9r7Odk+QEPXIAfqu+jo913UHkJ5aV52/9VtYa89lKAvAs6Me15PYNQ+VZuiEF8LgIhsAjYFn/aJSHUItU0mD2i7UINTwIFL/OYRaso+xyDr8xR+bxoLmUFh/TlH+n+TU4B885L7PO98J0IJ+smGuBP/iTpfm1BeGzio+gTwRAj1XJCI7D7fPFWssj7HB+tzfJiOPocS9PVAybjnxUBjiG28IbzWGGPMNAplMfj7QIWIlImIF7gH2DyhzWbgfglYDXSralOIrzXGGDONphzRq6pfRB4CthJYIvmUqlaJyAPB848DWwgsrawhsLzyixd67bT05Dcue/onClmf44P1OT6Evc8hraM3xhgTvew6fmOMiXEW9MYYE+NiIuhF5LMiUiUiYyKyYsK5PxeRGhGpFpFPOFXjdBCR9cF+1YjIw07XM11E5CkRaRGRw+OO5YjIqyJyPPhntpM1hpOIlIjIGyJyJPj3+mvB4zHbZwARSRKRXSJyINjvvw4ej/V+u0Vkn4j8Kvg87P2NiaAHDgOfAd4ef1BEFhFY6bMYWA98N7gtQ9Qbt73EBmARcG+wv7HoXwn8/MZ7GHhNVSuA14LPY4Uf+IaqXgmsBh4M/mxjuc8Aw8DNqroMuBpYH1zFF+v9/hpwZNzzsPc3JoJeVY+o6mRX0m4EnlPVYVU9QWBV0MqZrW7afLg1har6gHPbS8QcVX0b6JhweCPwo+DXPwLumtGippGqNp3bFFBVewmEQBEx3GcADegLPk0IPpQY7reIFAN3At8fdzjs/Y2JoL+A823NEAtiuW+hmBW8VoPgnwUO1zMtRKQUuAbYSRz0OTiNsR9oAV5V1Vjv9z8DfwqM36Qr7P2Nmv3oReTXwGQ3Zv1LVf3l+V42ybFYWU8ay30zgIikAT8Hvq6qPfGw4Z6qjgJXi0gW8IKILHG6pukiIp8EWlR1j4jcOJ3vFTVBr6q3XsLLQtm+IVrFct9C0SwiharaFNwptcXpgsJJRBIIhPxPVPX54OGY7vN4qtolIm8S+GwmVvu9FviUiNwBJAEZIvJjpqG/sT51sxm4R0QSRaSMwH75uxyuKVzifXuJzcDng19/Hjjfb3VRJ3gjnx8AR1T1H8editk+A4hIfnAkj4gkA7cCR4nRfqvqn6tqsaqWEvj/93VV/X2mo7+qGvUP4NMERrjDQDOwddy5vwRqgWpgg9O1hrnfdwDHgv37S6frmcZ+Pgs0ASPBn/OXgVwCKxKOB//McbrOMPb3egLTcAeB/cHHHbHc52C/lwL7gv0+DPz34PGY7newjzcCv5qu/toWCMYYE+NiferGGGPingW9McbEOAt6Y4yJcRb0xhgT4yzojTEmxlnQG2NMjLOgN8aYGPf/AVBv9JDHS/4TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for medium\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create distribution plot\n",
    "sns.distplot(medium_sample)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for large\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create distribution plot\n",
    "sns.distplot(large_sample)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro-Wilk Test for Small Sample:  ShapiroResult(statistic=0.8959227204322815, pvalue=0.19751997292041779)\n",
      "Shapiro-Wilk Test for Medium Sample:  ShapiroResult(statistic=0.9774233102798462, pvalue=0.08356800675392151)\n",
      "Shapiro-Wilk Test for Large Sample:  ShapiroResult(statistic=0.9972043633460999, pvalue=0.08009752631187439)\n"
     ]
    }
   ],
   "source": [
    "# Import shapiro funtion\n",
    "from scipy.stats import shapiro\n",
    "\n",
    "# Apply Shapiro-Wilk Test\n",
    "print(\"Shapiro-Wilk Test for Small Sample: \",shapiro(small_sample))\n",
    "print(\"Shapiro-Wilk Test for Medium Sample: \",shapiro(medium_sample))\n",
    "print(\"Shapiro-Wilk Test for Large Sample: \",shapiro(large_sample))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anderson-Darling Test for Small Sample:  AndersonResult(statistic=0.4197448373978485, critical_values=array([0.501, 0.57 , 0.684, 0.798, 0.95 ]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))\n",
      "Anderson-Darling Test for Medium Sample:  AndersonResult(statistic=0.31345544965506633, critical_values=array([0.555, 0.632, 0.759, 0.885, 1.053]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))\n",
      "Anderson-Darling Test for Large Sample:  AndersonResult(statistic=0.4224054571993747, critical_values=array([0.574, 0.653, 0.784, 0.914, 1.088]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))\n"
     ]
    }
   ],
   "source": [
    "# Import anderson funtion\n",
    "from scipy.stats import anderson\n",
    "\n",
    "# Apply Anderson-Darling Test\n",
    "print(\"Anderson-Darling Test for Small Sample: \",anderson(small_sample))\n",
    "print(\"Anderson-Darling Test for Medium Sample: \",anderson(medium_sample))\n",
    "print(\"Anderson-Darling Test for Large Sample: \",anderson(large_sample))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D'Agostino-Pearson Test for Small Sample:  NormaltestResult(statistic=1.805797444152904, pvalue=0.4053928337620476)\n",
      "D'Agostino-Pearson Test  for Medium Sample:  NormaltestResult(statistic=9.67159870491099, pvalue=0.007940338658286777)\n",
      "D'Agostino-Pearson Test  for Large Sample:  NormaltestResult(statistic=3.8086643750188633, pvalue=0.1489220614346304)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/avinash/anaconda3/lib/python3.8/site-packages/scipy/stats/stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    }
   ],
   "source": [
    "# Import normaltest function\n",
    "from scipy.stats import normaltest\n",
    "\n",
    "# Apply  D'Agostino-Pearson test\n",
    "print(\"D'Agostino-Pearson Test for Small Sample: \", normaltest(small_sample))\n",
    "print(\"D'Agostino-Pearson Test  for Medium Sample: \",normaltest(medium_sample))\n",
    "print(\"D'Agostino-Pearson Test  for Large Sample: \",normaltest(large_sample))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import required library\n",
    "import numpy as np\n",
    "from scipy.misc import face\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "face_image = face()\n",
    "mask_random_array = np.random.randint(0, 3, size=face_image.shape)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, ncols=2)\n",
    "\n",
    "# Display the Original Image \n",
    "plt.subplot(2,2,1)\n",
    "plt.imshow(face_image)\n",
    "plt.title(\"Original Image\")\n",
    "plt.axis('off')\n",
    "\n",
    "# Display masked array\n",
    "masked_array = np.ma.array(face_image, mask=mask_random_array)\n",
    "plt.subplot(2,2,2)\n",
    "plt.title(\"Masked Array\")\n",
    "plt.imshow(masked_array)\n",
    "plt.axis('off')\n",
    "\n",
    "# Log operation on original image\n",
    "plt.subplot(2,2,3)\n",
    "plt.title(\"Log Operation on Original\")\n",
    "plt.imshow(np.ma.log(face_image).astype('uint8'))\n",
    "plt.axis('off')\n",
    "\n",
    "# Log operation on masked array\n",
    "plt.subplot(2,2,4)\n",
    "plt.title(\"Log Operation on Masked\")\n",
    "plt.imshow(np.ma.log(masked_array).astype('uint8'))\n",
    "plt.axis('off')\n",
    "\n",
    "# Display the subplots\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
